Determining client system attributes

ABSTRACT

In general, webpage or other resource accesses by client systems may be recorded, and those accesses may be analyzed to develop audience measurement reports. At times, it may be desirable to segment those reports according to classes of client systems (e.g., work vs. home client systems). A given client system can be classed into one of the reporting classes based on one or more classes of network service providers that provide the client with access to a network. The recorded resource accesses and classes of the client systems can then be used to generate audience measurement reports that are segmented according to one or more of the client system classes.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.61/241,576, filed Sep. 11, 2009, and titled “Determining Client SystemAttributes”, the disclosure of which is considered part of (and isincorporated by reference in) the disclosure of this application.

BACKGROUND

Internet audience measurement may be useful for a number of reasons. Forexample, some organizations may want to be able to make claims about thesize and growth of their audiences or technologies. Similarly,understanding consumer behavior, such as how consumers interact with aparticular web site or group of web sites, may help organizations makedecisions that improve their traffic flow or the objective of theirsite. In addition, understanding Internet audience visitation and habitsmay be useful in supporting advertising planning, buying, and selling.

SUMMARY

In one general aspect, a system includes one or more processing devicesand one or more storage devices storing instructions that, when executedby the one or more processing devices, cause the one or more processingdevices to perform the following operations. A set of records isaccessed. Each of the records indicates an identifier of a resource thathas been accessed by a client system, a network address of the clientsystem that accessed the resource, a time that the client systemaccessed the resource, and a unique identifier for the client systemthat accessed the resource. Each record in at least a portion of theaccessed set of records is associated with one of multiple networkaccess providers based on the network address included in the record.For each of the multiple network access providers, a determination ismade as to whether the network access provider belongs to a home classor a work class based on the times included in the records associatedwith the network access provider. The unique identifiers in the recordsassociated with a network access provider are associated with the homeclass or work class based on the determined home class or work class forthe network access providers associated with the records that includethe unique identifier. One or more reports are generated based on atleast a portion of the records in the accessed set of records and thehome class or work class associated with each unique identifier. The oneor more reports include information regarding client system access ofone or more of the resources. The information regarding client systemaccess of one or more of the resources is segmented according to one orboth of the home and work classes.

In another general aspect a system includes one or more processingdevices, and one or more storage devices storing instructions that, whenexecuted by the one or more processing devices, cause the one or moreprocessing devices to perform the following operations. A set of recordsis accessed. Each of the records indicates an identifier of a resourcethat has been accessed by a client system over a network and a uniqueidentifier for the client system that accessed the resource. One ofmultiple classes is determined for each unique identifier based on theaccessed set of records. Each unique identifier is associated with theclass determined for the unique identifier. One or more reports aregenerated based on at least a portion of the records included in theaccessed set of records and the class associated with each uniqueidentifier. The one or more reports include information regarding clientsystem access of one or more of the resources. The information regardingclient system access of one or more of the resources is segmentedaccording to one or more of the multiple classes.

Implementations may include one or more of the following features. Forexample, each of the records may include a network address of the clientsystem that accessed the resource and a time that the client systemaccessed the resource. Determining one of multiple classes for eachunique identifier based on the accessed data may include determining oneof multiple classes for each unique identifier based on the networkaddresses and the times indicated in the records that indicate uniqueidentifiers.

Determining one of multiple classes for each unique identifier based onthe network addresses and the times indicated in the records thatindicate unique identifiers may include associating each record with oneof multiple network access providers based on the network addressincluded in the record. Determining one of multiple classes for eachunique identifier based on the network addresses and the times indicatedin the records that indicate unique identifiers may further includedetermining one of multiple classes for each network access providerbased on the times included in the records associated with the networkaccess provider. Determining one of multiple classes for each uniqueidentifier based on the network addresses and the times indicated in therecords that indicate unique identifiers may further include determiningone of multiple classes for each unique identifier based on the classesdetermined for the network access providers associated with the recordsthat indicate unique identifiers.

Determining one of multiple classes for each network access providerbased on the times indicated in the records associated with the networkaccess provider may include determining a first number of the recordsassociated with the network access provider that include a time during afirst time period. Determining one of multiple classes for each networkaccess provider based on the times indicated in the records associatedwith the network access provider may further include determining asecond number of the records associated with the network access providerthat include a time during a second time period. Determining one ofmultiple classes for each network access provider based on the timesindicated in the records associated with the network access provider mayfurther include determining a ratio between the first number and thesecond number. Determining one of multiple classes for each networkaccess provider based on the times indicated in the records associatedwith the network access provider may further include determining one ofmultiple classes for the network access provider based on the ratio.

The multiple classes may include a home class and a work class. Thefirst time period may include an evening period and the second timeperiod includes a daytime period.

Determining one of multiple classes for each unique identifier based onthe classes determined for the network access providers associated withthe records that indicate the unique identifier may determining that therecords that indicate the unique identifier are associated with a singlenetwork access provider. Determining one of multiple classes for eachunique identifier based on the classes determined for the network accessproviders associated with the records that indicate the uniqueidentifier may further include associating the class determined for thesingle network with the unique identifier.

Determining one of multiple classes for each unique identifier based onthe classes determined for the network access providers associated withthe records that indicate the unique identifier may include determiningthat a first portion of the records that indicate a unique identifierare associated with a first network access provider with a first classand a second portion of the records that indicate the unique identifierare associated with a second network access provider with a secondclass. Determining one of multiple classes for each unique identifierbased on the classes determined for the network access providersassociated with the records that indicate the unique identifier mayfurther include instructions determining whether a number of the recordsin the first portion exceeds a threshold. Determining one of multipleclasses for each unique identifier based on the classes determined forthe network access providers associated with the records that indicatethe unique identifier may further include associating the uniqueidentifier with the first class if the number of the records in thefirst portion exceeds the threshold. Determining one of multiple classesfor each unique identifier based on the classes determined for thenetwork access providers associated with the records that indicate theunique identifier may further include associating the unique identifierwith the second class if the number of the records in the first portiondoes not exceed the threshold.

Determining one of multiple classes for each unique identifier based onthe classes determined for the network access providers associated withthe records that indicate the unique identifier may include determiningan initial one of the multiple classes for a unique identifier based onthe classes determined for the network access providers associated withthe records that indicate the unique identifier. Determining one ofmultiple classes for each unique identifier based on the classesdetermined for the network access providers associated with the recordsthat indicate the unique identifier may further include accessing one ormore of the records that indicate the unique identifier. Determining oneof multiple classes for each unique identifier based on the classesdetermined for the network access providers associated with the recordsthat indicate the unique identifier may further include determining thatthe unique identifier belongs to a different one of the multiple classesbased on an analysis of the accessed one or more records that indicatethe unique identifier.

In another general aspect, a method includes accessing a set of records.Each of the records indicates an identifier of a resource that has beenaccessed by a client system over a network and a unique identifier forthe client system that accessed the resource. The method also includesdetermining one of multiple classes for each unique identifier based onthe accessed set of records and associating each unique identifier withthe class determined for the unique identifier. In addition, the methodincludes generating one or more reports based on at least a portion ofthe records included in the accessed set of records and the classassociated with each unique identifier. The one or more reports includeinformation regarding client system access of one or more of theresources. The information regarding client system access of one or moreof the resources is segmented according to one or more of the multipleclasses.

Implementations may include one or more of the following features. Forexample, each of the records may include a network address of the clientsystem that accessed the resource and a time that the client systemaccessed the resource. Determining one of multiple classes for eachunique identifier based on the accessed data may include determining oneof multiple classes for each unique identifier based on the networkaddresses and the times indicated in the records that indicate uniqueidentifiers.

Determining one of multiple classes for each unique identifier based onthe network addresses and the times indicated in the records thatindicate unique identifiers may include associating each record with oneof multiple network access providers based on the network addressincluded in the record. Determining one of multiple classes for eachunique identifier based on the network addresses and the times indicatedin the records that indicate unique identifiers may further includedetermining one of multiple classes for each network access providerbased on the times included in the records associated with the networkaccess provider. Determining one of multiple classes for each uniqueidentifier based on the network addresses and the times indicated in therecords that indicate unique identifiers may further include determiningone of multiple classes for each unique identifier based on the classesdetermined for the network access providers associated with the recordsthat indicate unique identifiers.

Determining one of multiple classes for each network access providerbased on the times indicated in the records associated with the networkaccess provider may include determining a first number of the recordsassociated with the network access provider that include a time during afirst time period. Determining one of multiple classes for each networkaccess provider based on the times indicated in the records associatedwith the network access provider may further include determining asecond number of the records associated with the network access providerthat include a time during a second time period. Determining one ofmultiple classes for each network access provider based on the timesindicated in the records associated with the network access provider mayfurther include determining a ratio between the first number and thesecond number. Determining one of multiple classes for each networkaccess provider based on the times indicated in the records associatedwith the network access provider may further include determining one ofmultiple classes for the network access provider based on the ratio.

The multiple classes may include a home class and a work class. Thefirst time period may include an evening period and the second timeperiod includes a daytime period.

Determining one of multiple classes for each unique identifier based onthe classes determined for the network access providers associated withthe records that indicate the unique identifier may determining that therecords that indicate the unique identifier are associated with a singlenetwork access provider. Determining one of multiple classes for eachunique identifier based on the classes determined for the network accessproviders associated with the records that indicate the uniqueidentifier may further include associating the class determined for thesingle network with the unique identifier.

Determining one of multiple classes for each unique identifier based onthe classes determined for the network access providers associated withthe records that indicate the unique identifier may include determiningthat a first portion of the records that indicate a unique identifierare associated with a first network access provider with a first classand a second portion of the records that indicate the unique identifierare associated with a second network access provider with a secondclass. Determining one of multiple classes for each unique identifierbased on the classes determined for the network access providersassociated with the records that indicate the unique identifier mayfurther include instructions determining whether a number of the recordsin the first portion exceeds a threshold. Determining one of multipleclasses for each unique identifier based on the classes determined forthe network access providers associated with the records that indicatethe unique identifier may further include associating the uniqueidentifier with the first class if the number of the records in thefirst portion exceeds the threshold. Determining one of multiple classesfor each unique identifier based on the classes determined for thenetwork access providers associated with the records that indicate theunique identifier may further include associating the unique identifierwith the second class if the number of the records in the first portiondoes not exceed the threshold.

Determining one of multiple classes for each unique identifier based onthe classes determined for the network access providers associated withthe records that indicate the unique identifier may include determiningan initial one of the multiple classes for a unique identifier based onthe classes determined for the network access providers associated withthe records that indicate the unique identifier. Determining one ofmultiple classes for each unique identifier based on the classesdetermined for the network access providers associated with the recordsthat indicate the unique identifier may further include accessing one ormore of the records that indicate the unique identifier. Determining oneof multiple classes for each unique identifier based on the classesdetermined for the network access providers associated with the recordsthat indicate the unique identifier may further include determining thatthe unique identifier belongs to a different one of the multiple classesbased on an analysis of the accessed one or more records that indicatethe unique identifier.

Implementations of any of the described techniques may include a methodor process, an apparatus, a device, a machine, a system, or instructionsstored on a computer-readable storage device. The details of particularimplementations are set forth in the accompanying drawings anddescription below. Other features will be apparent from the followingdescription, including the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a system in which site access data canbe obtained by including beacon code in one or more webpages.

FIG. 2 illustrates an example of a system that can classify clientsystems using site access data and use the classifications with the siteaccess data to generate measurement data segmented by one or more of theclasses.

FIG. 3 is a flow chart illustrating an example of a process that may beperformed to determine classes for client systems and generate accessreports that are segmented by one or more of the client system classes.

FIG. 4 is a flow chart illustrating a process that may be used todetermine those client systems associated with a home class that shouldinstead be associated with a work class.

FIG. 5 illustrates an example of a system in which a panel of users maybe used to obtain panel data for Internet audience measurement.

DETAILED DESCRIPTION

In general, webpage or other resource accesses by client systems may berecorded, and those accesses may be analyzed to develop audiencemeasurement reports. At times, it may be desirable to segment thosereports according to classes of client systems (e.g., work vs. homeclient systems). A given client system can be classed into one of thereporting classes based on one or more classes of network serviceproviders that provide the client with access to a network. The recordedresource accesses and classes of the client systems can then be used togenerate audience measurement reports that are segmented according toone or more of the client system classes.

Referring to FIG. 1, a system 100 includes one or more client systems102, one or more web servers 110, one or more collection servers 130,and one or more databases 132. The client systems 102, collection server130, and web servers 110 may communicate with one another over anetwork, such as the Internet, using various protocols, such as theHyperText Transfer Protocol (HTTP).

In general, users employ client systems 102 to access resources, such aswebpages, located at the web servers 110. As described more fully below,some or all of the accessed resources include beacon code, which causesthe client systems 102 to send information about these resource accessesto a collection server 130. This information may be used to understandthe usage habits of the users of the network (e.g., the Internet).

While the example shown in FIG. 1 only depicts one client system 102,the system 100 may include any number of client systems. Similarly,while the example shown in FIG. 1 only depicts a single collectionserver 130, system 100 may include multiple collection servers 130. Forexample, each of multiple client systems 102 may send data to more thanone collection server 130 for redundancy. In other implementations, theclient systems 102 may send data to different collection servers 130. Inthis implementation, the data may be communicated to and aggregated at acentral location for later processing. The central location may be oneof the collection servers. Also, while only a single web server 110 isdepicted, system 100 may include multiple web servers.

Each of the client systems 102, the collection servers 130, and the webservers 110 may be implemented using, for example, a general-purposecomputer capable of responding to and executing instructions in adefined manner, a personal computer, a special-purpose computer, aworkstation, a server, or a mobile device. Client systems 102,collection servers 130, and web servers 110 may receive instructionsfrom, for example, a software application, a program, a piece of code, adevice, a computer, a computer system, or a combination thereof, whichindependently or collectively direct operations. The instructions may beembodied permanently or temporarily in any type of machine, component,equipment, or other physical storage medium that is capable of beingused by a client system 102, collection server 130, and web server 110.

The client systems 102 include a browser application 104 that retrieveswebpages 106 from web servers 110 and renders the retrieved webpages106. Some of the webpages 106 include beacon code 108. In general,publishers of webpages may agree with the entity operating thecollection server 130 to include this beacon code in some or all oftheir webpages. This code 108 is rendered with the webpage 106 in whichthe code 108 is included. When rendered, the code 108 causes the browserapplication 104 to send a message to the collection server 130. Thismessage includes certain information, such as the URL of the webpage 106in which the beacon code 108 is included. For example, the beacon codemay be JavaScript code that accesses the URL of the webpage on which thecode is included, and sends to the collection server 130 an HTTP Postmessage that includes the URL in a query string. Similarly, the beaconcode may be JavaScript code that accesses the URL of the webpage onwhich the code is included, and includes the accessed URL in the “src”attribute of an <img> tag, which results in a request for the resourcelocated at the URL in the “src” attribute of the <img> tag being sent tothe collection server 130. Because the URL of the webpage is included inthe “src” attribute, the collection server 130 receives the URL of thewebpage. The collection server 130 can then return a transparent image.The following is an example of such JavaScript:

<script type=“text/javascript”> document.write(“<img id=‘img1’height=‘1’width=‘1’>”);document.getElementById(“img1”).src=“http://example.com/scripts/report.dll?C7=” + escape(window.location.href) + “&rn=” +Math.floor(Math.random( )*99999999); </script>

The message may also include a unique identifier for the client system.For example, when a client system first sends a beacon message to thecollection server 130, a unique identifier may be generated for theclient system (and associated with the received beacon message). Thatunique identifier may then be included in a cookie that is set on thatclient system 102. As a result, later beacon messages from that clientsystem may have the cookie appended to them such that the messagesinclude the unique identifier for the client system.

Thus, as users of client systems 102 access webpages (e.g., on theInternet), the client systems 102 access the webpages that include thebeacon code, which results in messages being sent to the collectionserver 130. These messages indicate the webpage that was accessed (e.g.,by including the URL for the webpage) and a unique identifier for theclient system that sent the message. When a message is received at thecollection server 130, a record may be generated for the receivedmessage. The record may indicate an identifier (e.g., the URL) of thewebpage accessed by the client system, the unique identifier for theclient system, a time at which the client system accessed the webpage(e.g., by including a time stamp of when the message was received by thecollection server 130), and a network address, such as an IP address, ofthe client system that accessed the webpage. The collection server 130may then aggregate these records and store the aggregated records in thedatabase 132 as site access data 132 a.

Reports can be generated from the aggregated records. For example, thisdata may be used to estimate the number of unique visitors visitingcertain webpages or groups of webpages. This data may also be used todetermine other estimates, such as the frequency of usage per clientsystem and average number of pages viewed per client system.

In certain situations, it may be desirable to segment such reportsaccording to a class of the client system. For example, publishers andadvertisers may want to see such reports segmented according to thetraffic that can be attributed to client systems used primarily for homeuse and the traffic that can be attributed to client systems usedprimarily for business reasons.

As described further below, implementations may identify these twosubpopulations (or other additional or alternative subpopulations) basedon the aggregated records. For example, the records may be analyzed todetermine a class for the network access provider that provides accessto the network for a given client system (as represented in the recordsby the unique identifier for the client system). The network accessprovider may be, for example, an Internet Service Provider (ISP) or anorganization such as a business. The class of the network accessprovider(s) then may be used to classify the client system. As a furtherexample, analysis of behaviors that can be detected in the records for agiven client system may be used additionally, or alternatively, to theclass of the network access provider in determining a class for theclient system.

Once the client systems are classified, the records can then besegmented into the subpopulations and used to generate reports beingsegmented into one or more of these classes. For example, a report maybe generated that indicates the unique home client systems that haveaccessed a certain webpage or webpages over a period time. Similarly, areport may be generated that indicates the unique work client systemsthat have accessed the same webpage or group of webpages over the sameor different time period. A report may be generated that shows both homeand work accesses, separated according to the categories.

Also, it may be desirable to exclude certain classes of client systemsfrom the reports. For instance, publishers and advertisers may not wantshared use clients systems (e.g., those in libraries or “cyber-cafes”)to be included in the reports. In this case, the records for thoseclient systems may be excluded by classifying those client systemsappropriately. Alternative, or additionally, it may be useful togenerate reports segmented by shared use client systems.

FIG. 2 illustrates an example of a system 200 that can be used toeffectively classify client systems using site access data byclassifying a unique identifier of the client system. Theseclassifications can be used with the site access data to generatemeasurement data segmented by one or more of the classes.

The system 200 includes a reporting server 202. The reporting server 202may be implemented using, for example, a general-purpose computercapable of responding to and executing instructions in a defined manner,a personal computer, a special-purpose computer, a workstation, aserver, or a mobile device. The reporting server 202 may receiveinstructions from, for example, a software application, a program, apiece of code, a device, a computer, a computer system, or a combinationthereof, which independently or collectively direct operations. Theinstructions may be embodied permanently or temporarily in any type ofmachine, component, equipment, or other physical storage medium that iscapable of being used by the reporting server 202.

The reporting server 202 executes instructions that implement aclassification module 204 and a report generation module 206. Theclassification module 204 includes a pre-processing module 204 a, anetwork access provider classification module 204 b, and a client systemclassification module 204 c. The classification processor 204 mayimplement a process, such as that shown in FIG. 3, that accesses thesite access data 132 a from the database 132, and classifies clientsystems (represented by the unique identifier of the client system) intoone of multiple classes based on this data. The report generation module206 may use the site access data 132 a and the classes of the clientsystems to generate one or more reports 208 that include informationregarding client system accesses of one or more of resources, with theinformation being segmented according to one or more of the classes.

FIG. 3 is a flow chart illustrating an example of a process 300 that maybe performed to determine classes for client systems and generateaccess/usage reports that are segmented by one or more of the clientsystem classes. In general, the network access providers used by theclient systems to access the network are classified and a given clientsystem's usage of one or more network access providers is then analyzedto assign one of multiple classes to the client system. The classes ofthe client systems can then be used to generate access/usage reportsthat are segmented by one or more of the classes associated with theclient systems.

The following describes process 300 as being performed by thepre-processing module 204 a, the network access provider classificationmodule 204 b, and the client system classification module 204 c.However, the process 300 may be performed by other systems or systemconfigurations. Furthermore, the following describes an example of aprocess that may be used when the reporting segments include work andhome. However, other implementations may employ additional, oralternative, classes/reporting segments.

The pre-processing module 204 a accesses the site access data 132 a(302). As described above, the site access data is formed based onmessages sent by client systems 102 as the client systems 102 accesswebpages that include beacon code, which results in messages being sentto the collection server 130. These messages indicate the webpage thatwas accessed and a unique identifier for the client system that sent themessage. When a message is received at the collection server 130, arecord may be generated for the received message. The record mayindicate an identifier (e.g., the URL) of the webpage accessed by theclient system, the unique identifier for the client system, a time atwhich the client system accessed the webpage (e.g., by including a timestamp of when the message was received as a proxy for the time thewebpage was accessed), and a network address, such as an IP address, ofthe client system that accessed the webpage. The collection server 130may then aggregate these records and store the aggregated records in thedatabase 132 as site access data 132 a.

As a result, the site access data 132 a includes a set of records. Eachof the records indicates an identifier of a resource that has beenaccessed by a client system, a network address of the client system thataccessed the resource, a time that the client system accessed theresource, and a unique identifier for the client system that accessedthe resource. The site access data 132 a that is accessed by thepre-processing module may be site access data 132 a that is aggregatedfor a certain, previous time period. For example, the accessed siteaccess data 132 a may be site access data 132 a aggregated over theprevious month.

The pre-processing module 204 a performs one or more pre-processingfunctions on the accessed site access data 132 a (304). For example, thepre-processing module 204 a may match some or all of the URLs (or otheridentifiers of resources) in the records to patterns in a dictionary ofthe Internet, which may organize various different URLs into digitalmedia properties, reflecting how the Internet companies operate theirbusinesses. Each pattern may be associated with a web entity, which maybe a webpage or collection of webpages that are logically groupedtogether in a manner that reflects how Internet companies operate theirbusiness. For example, the various webpages that are included in thefinance.yahoo.com domain may be logically grouped together into a singleweb entity (e.g., Yahoo Finance). The pre-processing module 204 a mayassociate a given record with the web entity associated with the patternmatching the URL in the record.

In addition, the pre-processing module 204 a may remove certain records.For instance, records that reflect non-human initiated requests (e.g.,requests made by a search index crawler or other robot) may be removed.In some implementations, records for certain types of client systemdevices may be removed. For instance, records for mobile devices may beremove. In some implementations, such records may be detected based onuser agent data sent with the beacon message and recorded in the record.In addition, records may be removed for client systems not in aparticular geographic area (e.g., if the reports are being generated fora particular geographic area, such as North America). The country andregion of the client system corresponding to the record may bedetermined based on a reverse lookup of the network address (e.g., areverse lookup of the IP address).

The network access provider classification module 204 b associatesrecords in the pre-processed site access data with network accessproviders based on the network addresses indicated by the records (306).For example, in one implementation in which the network addresses are IPaddresses, a reverse IP lookup service may be used to obtain the networkaccess provider to which the IP address is assigned. Furthermore, thereverse lookup may also provide additional information, such as thecountry (and region of the country) in which the client system islocated.

The network access provider classification module 204 b determinesclasses for the network access providers (308). The classes for thenetwork access providers may be determined, for example, based on therecords associated with the network access providers. For instance, insome implementations, the network access provider classification module204 b analyzes the times in records associated with a given networkaccess provider to assess the number of accesses during one or more timeperiods. The network access provider classification module 204 b usesthe number of accesses during the one or more time periods to determinethe class for that network access provider.

For example, to assess whether a network access provider is in the workclass or the home class, the network access provider classificationmodule 204 b may evaluate the number of accesses from that networkaccess provider that occur during a time period associated with a worktime period (e.g., 9-5 pm, Mon-Fri) and the number of accesses from thatnetwork access provider that occur during a time period associated witha home time period (e.g., 6-10 pm, Mon-Fri). If the number of accessesduring the work time is greater than the number of accesses during thehome time period by a certain amount, then the network access providermay be classified in the work class, and vice versa.

In one implementation, for instance, the network access providerclassification module 204 b converts the times in the records to a localtime based on the country and region information obtained by the reverselookup. For each of the network access providers, the network accessprovider classification module 204 b determines, based on the local timeinformation, the average number of records during the work time period(e.g., the average number during the past month) and the average numberof records during the home time period (e.g., the average over the pastmonth). Based on the ratio between the average number of records duringthe work time period and the average number of records during the hometime period, network access provider classification module 204 b assignsthe network access provider to the work class or the home class. Forexample, the network access provider may be classified in the home classif the ratio of the home time period to the work time period is greaterthan one, and instead be classified in the work class if this ratio isless than one.

In some implementations, some of the network access providers may beclassified as work or home based on the names of the network accessprovider determined, for example, using a reverse lookup. For example,some network access providers may be known to be home providers or workproviders and, accordingly, their names may be associated with aparticular class.

Furthermore, some implementations may additionally (or alternatively)include other classes of network access providers. For instance, theclasses may include shared use, hotels, or airports, ‘large’corporations, ‘small’ companies. A shared use class may represent accessproviders that provide access to shared use client systems, such asthose in libraries. A hotel or airport class may represent accessproviders that provide access at a hotel or airport, respectively. Alarge corporation or small company class may represent network accessproviders that are companies above a certain market capitalization orbelow a certain market capitalization, respectively, or that areotherwise divided into large and small based on particular criteria.

Techniques similar to those described above may be used to classify anetwork access provider into the other classes. For example, shared useclient systems may be used most during a certain time period that isdistinct from the time periods for work and home. In this case, if theaccesses during a shared use time period are sufficiently greater thanaccesses during the work and home time period, the network accessprovider may be classified in the shared use class. In addition, some ofthe network access providers may be classified into any given classbased on the names of the network access provider determined, forexample, using a reverse lookup.

The client system classification module 204 c classifies the clientsystems (e.g., using the unique identifier as a proxy) based on theclasses determined for the network access providers (310). A givenunique identifier is associated with one of the classes based on theclasses of the network access provider(s) associated with the recordsthat indicate the unique identifier. For example, if all of the recordsthat indicate a given unique identifier are associated with a networkaccess provider or providers that have a single class aligned with oneof the reporting classes (e.g., all of the providers are classified ashome, which is one of the reporting classes), then the unique identifiermay be associated with that class (e.g., home).

A given unique identifier may be indicated in records associated withmore than one network access provider. For example, if the client deviceis a laptop, then a user may use the laptop on different network accessproviders. In that case, the client system's usage of different classesof network access providers may be analyzed to determine which reportingclass to assign to the client system.

For instance, if some of the records that indicate the unique identifierare associated with a network access provider in one class aligned withone of the reporting classes, while other records that indicate theunique identifier are associated with a network access provider in adifferent class that is aligned with another one of the reportingclasses, then a decision algorithm may be applied to the records todetermine which class to associate with the unique identifier. Forexample, if the number of records associated with the network accessprovider in one of the classes exceeds a threshold, then thecorresponding unique identifier may be associated with that class. Thismay be used, for example, when the classes are home and work. When thenumber of records associated with network access providers in the workclass exceeds a threshold, the unique identifier may be associated withthe work class, even if some records are associated with network accessproviders in the home class. As another example, the class of thenetwork access provider or providers associated with the most recordsfor a given unique identifier may be associated with the uniqueidentifier.

As a further example, if the records indicate the unique identifier isassociated with network access providers that have classes not alignedwith the reporting classes (either in addition or instead of providerswith classes aligned with the reporting classes), the collective set ora subset of the classes and/or the usage pattern of those classes may beused to assign a reporting class to the client system. For example, if aclient system is noted in a work class, home class, airport class, andhotel class, then it may be assumed that the client system is being usedby someone who travels for work and therefore classified in the workclass.

In some implementations, client system classification module 204 c mayperform additional processing to verify the class of a uniqueidentifier. For example, the client system classification module 204 cmay analyze the records that indicate unique identifiers associated withthe home class to insure those unique identifiers should continue to beclassified as home. This may be used to identify small office/homeoffice (SOHO) client systems. Such client systems may be providednetwork access by predominantly home network access providers. However,these machines are typically used primarily for business purposes,rather than home purposes and, accordingly, may properly be classifiedin the work class. The client system classification module 204 may use aprocess, such as process 400 described with respect to FIG. 4, todetermine those unique identifiers associated with the home class thatshould instead be associated with the work class. The client systemclassification module 204 c may then change the class associated withthese unique identifiers appropriately.

The report generation module 206 can then generate reports withinformation about access of one or more of the resources (410). Thisinformation may be segmented according to one or more of the classesassigned to the client systems. For example, a report may be generatedthat shows the total page views for a web entity (described above) overthe past month by client systems in the home class. To do so, forexample, the report generation module 206 may access the pre-processedsite access data and the information indicating which unique identifiersare associated with the home class. The report generation module 206then may use this information to tally the records associated with theweb entity and that indicate the unique identifiers associated with thehome class. The report generation module 206 may create a similar reportfor total unique page views by only counting accesses by a given uniqueentity a single time.

The report generation module 206 may create similar reports for clientsystems in the work class, and may also do so for client systems in theshared use class, if desired. Also, the report generation module 206 maycreate reports with other information with respect to a webpage, groupof webpages, or web entity, such as the frequency of access per clientsystem and average number of pages viewed per client system. Thesereports can also be segmented according to one or more of the classes.

FIG. 4 is a flow chart illustrating a process 400 that may be used todetermine those client systems (represented by the unique identifiers)associated with the home class that should instead be associated withthe work class. The following describes process 400 as being performedby the client system classification module 204 c. However, the process400 may be performed by other systems or system configurations.

To determine whether a given client system classified in the home classshould be instead classified in the work class, the client systemclassification module 204 c may access the records that indicate theunique identifier for the client system (402). Based on the accessedrecords, the client system classification module 204 c determinesbehavioral features for the client (404). For example, the time of daythe client system is most used, or other time of day usage patterns maybe determined. Also, the types of websites accessed (e.g., news sites,stock sites, video game sites) and absolute or relative amount of accessfor each type may be determined and used as a behavioral feature. Ifclasses of network access providers in addition, or as an alternative,to work and home are determined, the other classes and, potentially, thepatterns of use of those other classes of network access providers maybe determined and used as behavioral features.

The client system classification module 204 c determines whether theclient system is a SOHO system based on the behavioral features. Forexample, the client system classification module 204 c may apply a setof heuristic rules to the behavioral features to determine whether theclient system is a SOHO system.

Alternatively, or additionally, client system classification module 204c may use a machine learning classifier, such as a Bayesian or SupportVector Machine (SVM) classifier, to determine whether the client systemis a SOHO system. The classifier may be trained using behavioralfeatures for client systems known to be work systems and client systemsknown to be home systems. For instance, behavioral features of clientsystems previously determined to be work or home using the techniquesdescribed above may be used. Some of those client systems mayerroneously be classified as home client systems because they are SOHOsystems. However, the proportion of erroneously classified clientsystems may be small such that, in the aggregate, the behavioralfeatures of the client systems classified as home client systems mayaccurately reflect the behaviors of those client systems that belong tothe home class. As a result, the erroneously classified client systemsmay not have a significant impact on the training of the classifier.

The behavioral features determined based on the accessed records for theclient system being assessed are input into the classifier. Based on thebehavioral characteristics, the classifier generates an indication ofwhether the client system belongs to the work class or not, therebyindicating whether or not the client system is a SOHO system.

The client system classification module 204 c reclassifies the clientsystem into the work class if the client system is determined to be aSOHO system (408). For example, if the heuristic rules indicate that theclient system is a SOHO system, the system classification module 204 cassociates the unique identifier with the work class instead of the homeclass. Similarly, as another example, if a machine learning classifierindicates the client system belongs to the work class, the client systemclassification module 204 c associates the unique identifier with thework class instead of the home class.

In some implementations, the site access data, and associatedclassifications of unique identifiers/client systems in the site accessdata, may be used with panel data obtained from a panel of users togenerate reports regarding the accesses of one or more resources. Forexample, as described further below, the panel data may be used tosupplement the site access data when generating reports. Alternatively,or additionally, the site access data and associated classifications maybe used to set weighting targets when determining projection weights formembers of the panel.

FIG. 5 illustrates an example of a system 500 in which a panel of usersmay be used to obtain panel data for audience measurement (e.g.,Internet audience measurement). The system 500 includes client systems512, 514, 516, and 518, one or more web servers 510, a collection server530 (which may or may not be the same as collection server 130), and adatabase 532 (which may or may not be the same as database 132). Ingeneral, the users in the panel employ client systems 512, 514, 516, and518 to access resources on the Internet, such as webpages, located atthe web servers 510. Information about this resource access is sent by apanel application on each client system 512, 514, 516, and 518 to thecollection server 530. This information may be used to understand theusage habits of the users of the Internet in conjunction with, orseparately from, the site access data 132 a.

Each of the client systems 512, 514, 516, and 518, the collection server530, and the web servers 510 may be implemented using, for example, ageneral-purpose computer capable of responding to and executinginstructions in a defined manner, a personal computer, a special-purposecomputer, a workstation, a server, or a mobile device. Client systems512, 514, 516, and 518, collection server 530, and web servers 510 mayreceive instructions from, for example, a software application, aprogram, a piece of code, a device, a computer, a computer system, or acombination thereof, which independently or collectively directoperations. The instructions may be embodied permanently or temporarilyin any type of machine, component, equipment, or other physical storagemedium that is capable of being used by a client system 512, 514, 516,and 518, collection server 530, and web servers 510.

In the example shown in FIG. 5, the system 500 includes client systems512, 514, 516, and 518. However, in other implementations, there may bemore or fewer client systems. Similarly, in the example shown in FIG. 5,there is a single collection server 530. However, in otherimplementations there may be more than one collection server 530. Forexample, each of the client systems 512, 514, 516, and 518 may send datato more than one collection server for redundancy. In otherimplementations, the client systems 512, 514, 516, and 518 may send datato different collection servers. In this implementation, the data, whichrepresents data from the entire panel, may be communicated to andaggregated at a central location for later processing. The centrallocation may be one of the collection servers.

The users of the client systems 512, 514, 516, and 518 are a group ofusers that are a representative sample of the larger universe beingmeasured, such as the universe of all Internet users or all Internetusers in a geographic region. To understand the overall behavior of theuniverse being measured, the behavior from this sample is projected tothe universe being measured. The size of the universe being measuredand/or the demographic composition of that universe may be obtained, forexample, using independent measurements or studies. For example,enumeration studies may be conducted monthly (or at other intervals)using random digit dialing.

The users in the panel may be recruited by an entity controlling thecollection server 530, and the entity may collect various demographicinformation regarding the users in the panel, such as age, sex,household size, household composition, geographic region, and householdincome. The techniques chosen to recruit users may be chosen ordeveloped to help insure that a good random sample of the universe beingmeasured is obtained, biases in the sample are minimized, and thehighest manageable cooperation rates are achieved. Once a user isrecruited, a monitoring application is installed on the user's clientsystem to collect the information about the user's use of the clientsystem to access resources on the Internet and to send that informationto the collection server 530.

For example, the monitoring application may have access to the networkstack of the client system on which the monitoring application isinstalled. The monitoring application may monitor network traffic toanalyze and collect information regarding requests for resources sentfrom the client system and subsequent responses. For instance, themonitoring application may analyze and collect information regardingHTTP requests and subsequent HTTP responses.

Thus, in system 500, a monitoring application 512 b, 514 b, 516 b, and518 b, also referred to as a panel application, is installed on each ofthe client systems 512, 514, 516, and 518. Accordingly, when a user ofone of the client systems 512, 514, 516, or 518 employs, for example, abrowser application 512 a, 514 a, 516 a, or 518 a to visit and viewwebpages, information about these visits may be collected and sent tothe collection server 530 by the monitoring application 512 b, 514 b,516 b, and 518 b. For instance, the monitoring application may collectand send to the collection server 530 the URLs of webpages or otherresources accessed, the times those pages or resources were accessed,and an identifier associated with the particular client system on whichthe monitoring application is installed (which may be associated withthe demographic information collected regarding the user or users ofthat client system). The collection server 530 receives and records thisinformation. The collection server 530 aggregates the recordedinformation from the client systems and stores this aggregatedinformation in the database 532 as panel data 532 a.

The panel data 532 a may be analyzed to determine the visitation orother habits of users in the panel, which may be extrapolated to thelarger population of all Internet users. The information collectedduring a particular time period (session) can be associated with aparticular user of the client system (and/or his or her demographics)that is believed or known to be using the client system during that timeperiod. For example, the monitoring application may require the user toidentify his or herself, or techniques such as those described in U.S.Patent Application No. 2004-0019518 or U.S. Pat. No. 7,260,837, bothincorporated herein by reference, may be used. Identifying theindividual using the client system may allow the usage information to bedetermined and extrapolated on a per person basis, rather than a permachine basis. In other words, doing so allows the measurements taken tobe attributable to individuals across machines within households, ratherthan to the machines themselves.

To extrapolate the usage of the panel members to the larger universebeing measured, some or all of the members of the panel are weighted andprojected to the larger universe. In some implementations, a subset ofall of the members of the panel may be weighted and projected. Forinstance, analysis of the received data may indicate that the datacollected from some members of the panel may be unreliable. Thosemembers may be excluded from reporting and, hence, from being weightedand projected.

The reporting sample (those included in the weighting and projection)are weighted to insure that the reporting sample reflects thedemographic composition of the universe to be measured, and thisweighted sample is projected to the universe. This may be accomplishedby determining a projection weight for each member of the reportingsample and applying that projection weight to the usage of that member.

As described in U.S. Provisional Patent Application Ser. No. 61/175,941,titled “Determining Projection Weights Based On Census Data,” which isincorporated herein in its entirety, the projection weight for eachmember may be determined by taking into account site access datacollected by including beacon code in one or more webpages served by theweb servers 510. In particular, the site access data may be used to setweighting targets when determining projection weights for the paneldata.

As described in U.S. Provisional Patent Application Ser. No. 61/175,941,when using site access data (referred to therein as site centric data)to determine the projection weights for use with the panel data(referred to therein as panel centric data), it may be desirable toalign the site access data with the panel data according to the home andwork subpopulations. These two subpopulations can be identified in thepanel in a similar fashion to demographic collection (e.g., throughself-reporting).

Employing the techniques described in U.S. Provisional PatentApplication Ser. No. 61/175,941, the records from the site access datathat are associated with the home class may be used to set the weightingtargets for those members in the panel using home client systems, andthese weighting targets may be used to determine the projection weightsfor members in the home subpopulation. Similarly, employing thetechniques described in U.S. Provisional Patent Application Ser. No.61/175,941, the records from the site access data that are associatedwith the work class may be used to set the weighting targets for thosemembers in the panel using work client systems, and these weightingtargets may be used to determine the projection weights for members inthe work subpopulation.

The usage behavior of the weighted and projected sample (eithercollectively or segmented by home and work subpopulations) is thenconsidered a representative portrayal of the behavior of the defineduniverse. Behavioral patterns observed in the weighted, projected sampleare assumed to reflect behavioral patterns in the universe.

Reports can be generated from this information. For example, this datamay be used to estimate the number of unique visitors visiting certainwebpages or groups of webpages, or unique visitors within a particulardemographic visiting certain webpages or groups of webpages. This datamay also be used to determine other estimates, such as the frequency ofusage per user, average number of pages viewed per user, and averagenumber of minutes spent per user. These reports may be segmented byclasses, such as work and home.

Regardless of whether site access data is used to determine theprojection weights, the projected sample (either collectively orsegmented by home and work subpopulations) may be employed with the siteaccess data when generating reports. For example, the projected samplemay be used to generate estimates regarding client system access ofwebpages that do not employ the beacon code, while the site access datais employed to generate estimates for those webpages using the beaconcode. This may be employed, for instance, to generate a report regardinga particular web entity when some webpages that are part of the webentity employ the beacon code, while other webpages that are part of theweb entity do not.

The techniques described herein can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The techniques can be implemented as a computerprogram product, i.e., a computer program tangibly embodied in aninformation carrier, e.g., in a machine-readable storage device, inmachine-readable storage medium, in a computer-readable storage deviceor, in computer-readable storage medium for execution by, or to controlthe operation of, data processing apparatus, e.g., a programmableprocessor, a computer, or multiple computers. A computer program can bewritten in any form of programming language, including compiled orinterpreted languages, and it can be deployed in any form, including asa stand-alone program or as a module, component, subroutine, or otherunit suitable for use in a computing environment. A computer program canbe deployed to be executed on one computer or on multiple computers atone site or distributed across multiple sites and interconnected by acommunication network.

Processes can be performed by one or more programmable processorsexecuting a computer program to perform functions of the processes byoperating on input data and generating output. Processes can also beperformed by, and apparatus of the techniques can be implemented as,special purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The general elements of a computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. A computer may also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, such as, magnetic,magneto-optical disks, or optical disks. Information carriers suitablefor embodying computer program instructions and data include all formsof volatile or non-volatile memory, including by way of examplesemiconductor memory devices, such as, EPROM, EEPROM, and flash memorydevices; magnetic disks, such as, internal hard disks or removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated inspecial purpose logic circuitry.

A number of implementations of the techniques have been described.Nevertheless, it will be understood that various modifications may bemade. For example, useful results still could be achieved if steps ofthe disclosed techniques were performed in a different order and/or ifcomponents in the disclosed systems were combined in a different mannerand/or replaced or supplemented by other components.

Accordingly, other implementations are within the scope of the followingclaims.

What is claimed is:
 1. A system comprising: a plurality of clientsystems configured to render one or more web resources, and, in responseto rendering a web resource, generate and transmit a beacon messageindicating that the web resource has been rendered; one or moreprocessing devices; one or more storage devices storing instructionsthat, when executed by the one or more processing devices, causes theone or more processing devices to perform the following operations:receive, from the plurality of client systems, a plurality of beaconmessages, each beacon message having been sent from a client system as aresult of the client rendering a web resource, the beacon messageincluding an identifier of the rendered web resource and a uniqueidentifier of the client system; for each of the plurality of receivedbeacon messages, generate a record from a received beacon message, thegenerated record including the identifier of the web resource includedin the beacon message, the unique identifier of the client systemincluded in the beacon message, a time at which the beacon message wasreceived, and a network address from which the beacon message is sent,wherein the unique identifier is different and independent from thenetwork address; access a set of the generated records, each of thegenerated records in the accessed set including the unique identifier ofa first client system; associate each record in the accessed set ofrecords with at least one of multiple network access providers based onthe network address included in the record; for each of the multiplenetwork access providers, determine whether the network access providerbelongs to a home class or a work class based on evaluating a firstnumber of records associated with the network access provider thatinclude a time in a first time period and a second number of recordsassociated with the network access provider that include a time in asecond time period, the first time period being different from thesecond time period; and classify the unique identifier of the firstclient system with the home class or work class based on the determinedhome class or work class for the network access providers associatedwith the accessed records.
 2. The system of claim 1, wherein the storedinstructions further comprise instructions that, when executed by theone or more processing devices, cause the one or more processing devicesto perform the following operations: determine that a first of thenetwork access providers determined to belong to a first class isassociated with a first network address included in a first portion ofthe accessed records including the unique identifier of the first clientsystem and a second of the network access providers determined to belongto a second class is associated with a second network address includedin a second portion of the accessed records including the uniqueidentifier of the first client system, wherein the first class isdifferent from the second, wherein, to classify the unique identifier ofthe first client system, the instructions include instructions thatcause the one or more processing devices to classify the uniqueidentifier of the first client system into one of the multiple classesbased on the first class determined for the first network provider, thesecond class determined for the second network provider, andcharacteristics of the accessed records.
 3. The system of claim 1wherein, to determine whether the network access provider belongs to ahome class or a work class, the instructions include instructions thatcause the one or more processing devices to: calculate a ratio betweenthe first number and the second number; and determine one of multipleclasses for the network access provider based on the calculated ratio.4. The system of claim 1 wherein the first time period includes anevening period and the second time period includes a daytime period. 5.The system of claim 2 wherein, to classify the unique identifier of thefirst client system into one of the multiple classes, the instructionsinclude instructions that cause the one or more processing devices to:determine whether a number of the records in the first portion exceeds athreshold; classify the unique identifier of the first client systeminto the first class if the number of the accessed records in the firstportion exceeds the threshold; and classify the unique identifier of thefirst client system into the second class if the number of the accessedrecords in the first portion does not exceed the threshold.
 6. Thesystem of claim 2 wherein, to classify the unique identifier of thefirst client system into one of the multiple classes, the instructionsinclude instructions that cause the one or more processing devices to:classify the unique identifier of the first client system into the firstof the multiple classes based on the first class determined for thefirst network provider, the second class determined for the secondnetwork provider, and characteristics of the accessed records; analyzebehavioral features the accessed records; and reclassify the uniqueidentifier into a different one of the multiple classes based on theanalysis of the behavioral features of the accessed records.
 7. A methodcomprising: receiving, from a plurality of client systems and at acollection server, a plurality of beacon messages, each beacon messagehaving been sent from a client system as a result of the clientrendering a web resource, the beacon message including an identifier ofthe rendered web resource and a unique identifier of the client system;for each of the plurality of received beacon messages, generating, withthe collection server, a record from a received beacon message, thegenerated record including the identifier of the web resource includedin the beacon message, the unique identifier of the client systemincluded in the beacon message, a time at which the beacon message wasreceived, and a network address from which the beacon message is sent,wherein the unique identifier is different and independent from thenetwork address; accessing, with the collection server, a set of thegenerated records, each of the generated records in the accessed setincluding the unique identifier of a first client system; associating,with the collection server, each record in the accessed set of recordswith at least one of multiple network access providers based on thenetwork address included in the record; for each of the multiple networkaccess providers, determining, with the collection server, whether thenetwork access provider belongs to a first class or a second class basedon evaluating a first number of records associated with the networkaccess provider that include a time in a first time period and a secondnumber of records associated with the network access provider thatinclude a time in a second time period, the first time period beingdifferent from the second time period; determining, with the collectionserver, that a first of the network access providers determined tobelong to a first class is associated with a first network addressincluded in a first portion of the accessed records and a second of thenetwork access providers determined to belong to a second class isassociated with a second network address included in a second portion ofthe accessed records, wherein the first class is different from thesecond class; and classifying, with the collection server, the uniqueidentifier of the first client system into one of the multiple classesbased on the first class determined for the first network provider, thesecond class determined for the second network provider, andcharacteristics of the accessed records.
 8. The method of claim 7,wherein determining whether the network access provider belongs to afirst class or a second class includes: calculating a ratio between thefirst number and the second number; and determining one of multipleclasses for the network access provider based on the calculated ratio.9. The method of claim 8, wherein the multiple classes include a homeclass and a work class.
 10. The method of claim 9, wherein the firsttime period includes an evening period and the second time periodincludes a daytime period.
 11. The method of claim 7, whereinclassifying the unique identifier of the first client system into one ofthe multiple classes includes: determining whether a number of therecords in the first portion exceeds a threshold; classifying the uniqueidentifier of the first client system into the first class if the numberof the accessed records in the first portion exceeds the threshold; andclassifying the unique identifier of the first client system into thesecond class if the number of the accessed records in the first portiondoes not exceed the threshold.
 12. The method of claim 7, whereinclassifying the unique identifier of the first client system into one ofthe multiple classes includes: classifying the unique identifier of thefirst client system into the first of the multiple classes based on thefirst class determined for the o first network provider, the secondclass determined for the second network provider, and characteristics ofthe accessed records; analyzing behavioral features the accessedrecords; and reclassifying the unique identifier into a different one ofthe multiple classes based on the analysis of the behavioral features ofthe accessed records.